Databricks-ML-Assoc Model Development Practice Question
A data scientist is using MLflow to log a model on Databricks. They want to ensure that the model can be loaded and used for inference in a different environment. Which two of the following are necessary components that must be included when logging the model to guarantee portability? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse optional metadata like input examples or run IDs with the core components needed for a model to load and run in a new environment.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The conda environment file (conda.yaml) that lists all dependencies.
For a model to be portable across environments, it must include a signature to define input/output schemas and a conda environment file to capture dependencies. These ensure that the model can be correctly loaded and executed elsewhere. Other elements like training data, run ID, or input examples are not required for the model to function.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The conda environment file (conda.yaml) that lists all dependencies.
Why this is correct
The conda environment file is critical for portability because it specifies the exact libraries and versions needed to run the model. When loading in a different environment, MLflow uses this file to recreate the environment. Without it, the model may fail to load due to missing or incompatible dependencies. Therefore, it is a necessary component for ensuring the model works as intended elsewhere.
- ✗
The MLflow run ID where the model was logged.
Why it's wrong here
While the run ID is used to locate the model artifact, it is not part of the model itself and is not required for the model to function in a different environment. Once the model is loaded, the run ID is irrelevant. Portability depends on the model's contents, not its origin. Therefore, the run ID is not a necessary component for ensuring the model can be loaded and used.
- ✗
The model's input example, which provides a sample input for testing.
Why it's wrong here
An input example is optional and not required for model portability. It can be useful for documentation or testing, but the model can be loaded and used without it. The essential elements are the model files, signature, and dependencies. An input example does not affect the ability to load or run the model in a different environment. Thus, it is not a necessary component.
- ✓
The model's signature, which defines the input and output schema.
Why this is correct
The model signature is essential for portability because it documents the expected input and output types, allowing the model to be used consistently across environments. It enables validation and correct handling of data during inference. Without a signature, loading may succeed but predictions could fail if input data does not match expectations. Thus, including a signature is a key component for reliable deployment.
- ✗
The training dataset used to fit the model.
Why it's wrong here
The training dataset is not required for model portability. MLflow models are designed to be self-contained for inference; they do not include the training data. Including the dataset would bloat the artifact and is not necessary for loading or scoring. Portability focuses on the model's code and dependencies, not the data used to train it. Thus, this is not a necessary component.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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